Methods for additively manufactured identification features

Techniques for integrating a machine-readable matrix with a component of a mechanical structure using three-dimensional (3-D) printing are disclosed. Such techniques include generating at least one data model representing the component, and projecting a matrix pattern identifying one or more features of the component onto a selected surface portion of the component to produce a modified data model for use as an input to a 3-D printer.

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Description
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of, and claims priority to, U.S. patent application Ser. No. 15/677,734, filed Aug. 15, 2017, entitled “METHODS AND APPARATUS FOR ADDITIVELY MANUFACTURED IDENTIFICATION FEATURES,” now U.S. Pat. No. 10,357,959 B2, which application is incorporated by reference in its entirety as if fully set forth herein.

BACKGROUND Field

The present disclosure relates generally to data preservation techniques, and more specifically to integrated data matrices in additive manufacturing for facilitating the identification and preservation of data relevant to additively manufactured parts.

Background

Manufacturers in various industries have increasingly used additive manufacturing (“AM”) as a means to produce more complex and cost-efficient components. AM systems, also described as three-dimensional (3-D) printers, can produce structures having geometrically complex shapes, including some shapes that are difficult or impossible to create with conventional manufacturing processes. While these AM capabilities have broad application, they are especially prevalent in industries involving vehicles, boats, aircraft, motorcycles, and other transport structures

While producing components used in such structures, designers typically create a 3-D representation or model of the component using a computer-aided-design (CAD) program or similar application. The designer may create a custom representation of such a structure and/or use 3-D models from libraries of standard parts. Thereafter, the designed parts may be additively manufactured for further use or for assembly in a larger structure as appropriate.

During the course of these activities, designers and manufacturers in general have become aware of the importance of maintaining accurate records and data relating to each of the multitude of AM parts. To track such parts more efficiently, manufacturers often use barcodes or other identifiers affixed directly on the part. The barcode or other identifier may include information relative to one or more features of the item, such as its purpose, date and country of origin, intended destination, operating or assembly instructions, replacement date, instructions for assembly into a larger structure, and other pertinent details.

With the use of AM, however, new challenges have arisen in how to best associate these types of relevant data with AM components. Ordinary barcodes may be inadequate for this purpose. While the CAD model of the component is in 3-D, ordinary barcode data is in 2-D. This means that a 2-D data matrix representing the barcode is not compatible with a 3-D model of the corresponding AM product.

Conventional attempts to overcome this problem have included applying a separately printed barcode as a flat adhesive label onto the part. Among other deficiencies, this technique is both manually intensive and vulnerable to possible fraudulent activities such as label replacement and counterfeiting. Furthermore, such labels are ordinarily less durable than the associated AM component and are therefore particularly vulnerable to damage, vandalism, and tampering. These vulnerabilities erode confidence in the reliability of the manually applied label.

Accordingly, new techniques are needed for facilitating the association of relevant data with AM components.

SUMMARY

Several aspects of integrating data into AM components will be described more fully hereinafter with reference to three-dimensional printing techniques.

One aspect of a method of integrating a machine-readable matrix with a component of a mechanical structure using three-dimensional (3-D) printing includes generating at least one data model representing the component, and projecting a matrix pattern identifying one or more features of the component onto a selected surface portion of the component to produce a modified data model for use as an input to a 3-D printer.

One aspect of a component for use in a mechanical structure and three-dimensional (3-D) printed based on at least one data model includes a 3-D structure configured to perform one or more intended functions when assembled into the mechanical structure, and a machine-readable 3-D matrix pattern integrated on a selected surface portion of the 3-D structure and configured to identify one or more features of the component.

It will be understood that other aspects of integrating data into AM components will become readily apparent to those skilled in the art from the following detailed description, wherein it is shown and described only several embodiments by way of illustration. As will be realized by those skilled in the art, integrating data into AM components are capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of integrating data into AM components will now be presented in the detailed description by way of example, and not by way of limitation, in the accompanying drawings, wherein:

FIG. 1 is a flow diagram illustrating an exemplary process of initiating 3-D printing.

FIGS. 2A-D illustrate an exemplary powder bed fusion (PBF) system during different stages of operation.

FIG. 3A is an example of a 2-D matrix code.

FIG. 3B is an example of a 2-D matrix code having a different polarity and that contains identical data to matrix code of FIG. 3A.

FIG. 4A shows an AM matrix projected to a 3-D surface facet in accordance with an aspect of the present disclosure.

FIG. 4B shows a 2-D matrix projected onto a relatively flat surface facet of the 3-D model through translation, scaling, and rotation.

FIG. 5 is an example of a code pitch dimension as measured on the 2-D matrix of FIG. 3A.

FIG. 6 is a cross-sectional view of an extruded 3-D printed matrix made integral to an AM component.

FIG. 7 is a sensor view of an extruded 3-D matrix 700 with a flat top upper surface.

FIG. 8 is a cross-sectional view of a 3-D printed matrix having varying surface textures.

FIG. 9 shows an illustration of a code-reading operation of a textured matrix in accordance with the present disclosure.

FIGS. 10A-C show an illustration of variations of the matrix code reading process from three perspectives.

FIG. 11 shows a cross section of a matrix that has been exposed to contamination.

 DETAILED DESCRIPTION

The detailed description set forth below in connection with the appended drawings is intended to provide a description of various exemplary embodiments of integrating data into AM components and is not intended to represent the only embodiments in which the invention may be practiced. The term “exemplary” used throughout this disclosure means “serving as an example, instance, or illustration,” and should not necessarily be construed as preferred or advantageous over other embodiments presented in this disclosure. The detailed description includes specific details for the purpose of providing a thorough and complete disclosure that fully conveys the scope of the invention to those skilled in the art. However, the invention may be practiced without these specific details. In some instances, well-known structures and components may be shown in block diagram form, or omitted entirely, in order to avoid obscuring the various concepts presented throughout this disclosure.

The use of 3-D printing in the context of composite tooling provides significant flexibility for enabling manufacturers of mechanical structures and mechanized assemblies to manufacture parts with complex geometries. For example, 3-D printing techniques provide manufacturers with the flexibility to design and build parts having intricate internal lattice structures and/or profiles that are not possible to manufacture via traditional manufacturing processes.

FIG. 1 is a flow diagram 100 illustrating an exemplary process of initiating an AM process. A data model of the desired 3-D object to be printed is rendered (step 110). A data model is a virtual design of the 3-D object. Thus, the data model may reflect the geometrical and structural features of the 3-D object, as well as its material composition. The data model may be created using a variety of methods, including 3D scanning, 3D modeling software, photogrammetry software, and camera imaging.

3D scanning methods for creating the data model may also use a variety of techniques for generating a 3-D model. These techniques may include, for example, time-of flight, volumetric scanning, structured light, modulated light, laser scanning, triangulation, and the like.

3-D modeling software, in turn, may include one of numerous commercially available 3-D modeling software applications. Data models may be rendered using a suitable computer-aided design (CAD) package, for example in an STL format. STL files are one example of a file format associated with commercially available CAD software. A CAD program may be used to create the data model of the 3-D object as an STL file. Thereupon, the STL file may undergo a process whereby errors in the file are identified and resolved.

Following error resolution, the data model can be “sliced” by a software application known as a slicer to thereby produce a set of instructions for 3-D printing the object, with the instructions being compatible and associated with the particular 3-D printing technology to be utilized (step 120). Numerous slicer programs are commercially available. Slicer programs convert the data model into a series of individual layers representing thin slices (e.g., 100 microns thick) of the object be printed, along with a file containing the printer-specific instructions for 3-D printing these successive individual layers to produce an actual 3-D printed representation of the data model.

A common type of file used for this purpose is a G-code file, which is a numerical control programming language that includes instructions for 3-D printing the object. The G-code file, or other file constituting the instructions, is uploaded to the 3-D printer (step 130). Because the file containing these instructions is typically configured to be operable with a specific 3-D printing process, it will be appreciated that many formats of the instruction file are possible depending on the 3-D printing technology used.

In addition to the printing instructions that dictate what and how an object is to be rendered, the appropriate physical materials necessary for use by the 3-D printer in rendering the object are loaded into the 3-D printer using any of several conventional and often printer-specific methods (step 140). Powder bed fusion (PBF), for example, is an AM technique that uses a laser or other power source, along with a deflector, to fuse powdered material by aiming the laser or power source automatically at points in space defined by a 3-D model and binding the material together to create a solid structure. PBF includes within its scope various specific types of AM methods. Selective laser melting (SLM) and selective laser sintering (SLS), for instance, are PBF techniques in which print materials may be loaded as powders into a powder bed defined by a build plate and bordering walls (see FIGS. 2A-D). Layers of powder are deposited in a controlled manner into the powder bed for the power source to selectively manipulate on a layer-by-layer basis. Depending on the type of 3-D printer, other techniques for loading printing materials may be used. For example, in fused deposition modelling (FDM) 3-D printers, materials are often loaded as filaments on spools, which are placed on one or more spool holders. The filaments are typically fed into an extruder apparatus which, in operation, heats the filament into a melted form before ejecting the material onto a build plate or other substrate.

Referring back to FIG. 1, the respective data slices of the 3-D object are then printed based on the provided instructions using the material(s) (step 150). In 3-D printers that use laser sintering, a laser scans a powder bed and melts the powder together where structure is desired, and avoids scanning areas where the sliced data indicates that nothing is to be printed. This process may be repeated thousands of times until the desired structure is formed, after which the printed part is removed from a fabricator. In fused deposition modelling, parts are printed by applying successive layers of model and support materials to a substrate. In general, any suitable 3-D printing technology may be employed for purposes of this disclosure.

FIGS. 2A-D illustrate respective side views of an exemplary PBF system 200 during different stages of operation. As noted above, the particular embodiment illustrated in FIGS. 2A-D is one of many suitable examples of a PBF system employing principles of this disclosure. It should also be noted that elements of FIGS. 2A-D and the other figures in this disclosure are not necessarily drawn to scale, but may be drawn larger or smaller for the purpose of better illustration of concepts described herein. PBF system 200 can include a depositor 201 that can deposit each layer of metal powder, an energy beam source 203 that can generate an energy beam, a deflector 205 that can apply the energy beam to fuse the powder, and a build plate 207 that can support one or more build pieces, such as a build piece 209. PBF system 200 can also include a build floor 211 positioned within a powder bed receptacle. The walls of the powder bed receptacle 212 generally define the boundaries of the powder bed receptacle, which is sandwiched between the walls 212 from the side and abuts a portion of the build floor 211 below. Build floor 211 can progressively lower build plate 207 so that depositor 201 can deposit a next layer. The entire mechanism may reside in a chamber 213 that can enclose the other components, thereby protecting the equipment, enabling atmospheric and temperature regulation and mitigating contamination risks. Depositor 201 can include a hopper 215 that contains a powder 217, such as a metal powder, and a leveler 219 that can level the top of each layer of deposited powder.

Referring specifically to FIG. 2A, this figure shows PBF system 200 after a slice of build piece 209 has been fused, but before the next layer of powder has been deposited. In fact, FIG. 2A illustrates a time at which PBF system 200 has already deposited and fused slices in multiple layers, e.g., 150 layers, to form the current state of build piece 209, e.g., formed of 150 slices. The multiple layers already deposited have created a powder bed 221, which includes powder that was deposited but not fused.

FIG. 2B shows PBF system 200 at a stage in which build floor 211 can lower by a powder layer thickness 232. The lowering of build floor 211 causes build piece 209 and powder bed 221 to drop by powder layer thickness 232, so that the top of the build piece and powder bed are lower than the top of powder bed receptacle wall 212 by an amount equal to the powder layer thickness. In this way, for example, a space with a consistent thickness equal to powder layer thickness 232 can be created over the tops of build piece 209 and powder bed 221.

FIG. 2C shows PBF system 200 at a stage in which depositor 201 is positioned to deposit powder 217 in a space created over the top surfaces of build piece 209 and powder bed 221 and bounded by powder bed receptacle walls 212. In this example, depositor 201 progressively moves over the defined space while releasing powder 217 from hopper 215. Leveler 219 can level the released powder to form a powder layer 225 that has a thickness substantially equal to the powder layer thickness 232 (see FIG. 2B). Thus, the powder in a PBF system can be supported by a powder support structure, which can include, for example, a build plate 207, a build floor 211, a build piece 209, walls 212, and the like. It should be noted that the illustrated thickness of powder layer 225 (i.e., powder layer thickness 232 (FIG. 2B)) is greater than an actual thickness used for the example involving 150 previously-deposited layers discussed above with reference to FIG. 2A.

FIG. 2D shows PBF system 200 at a stage in which, following the deposition of powder layer 225 (FIG. 2C), energy beam source 203 generates an energy beam 227 and deflector 205 applies the energy beam to fuse the next slice in build piece 209. In various exemplary embodiments, energy beam source 203 can be an electron beam source, in which case energy beam 227 constitutes an electron beam. Deflector 205 can include deflection plates that can generate an electric field or a magnetic field that selectively deflects the electron beam to cause the electron beam to scan across areas designated to be fused. In various embodiments, energy beam source 203 can be a laser, in which case energy beam 227 is a laser beam. Deflector 205 can include an optical system that uses reflection and/or refraction to manipulate the laser beam to scan selected areas to be fused.

In various embodiments, the deflector 205 can include one or more gimbals and actuators that can rotate and/or translate the energy beam source to position the energy beam. In various embodiments, energy beam source 203 and/or deflector 205 can modulate the energy beam, e.g., turn the energy beam on and off as the deflector scans so that the energy beam is applied only in the appropriate areas of the powder layer. For example, in various embodiments, the energy beam can be modulated by a digital signal processor (DSP).

As noted above, parts that are additively manufactured are typically designed as part of a 3-D data model. By contrast, typical data matrices are a mathematical model of a flat, two-dimensional, binary black-and-white surface pattern similar to a printed paper label. These properties make such a data representation incompatible for integration into AM components. To avoid such incompatibilities and the conventional deficiencies discussed above with respect to conventional solutions, the method herein broadly contemplates directly associating the matrix permanently into the AM part in an immutable way.

FIG. 3A is an example of a 2-D matrix code 300. Matrix codes are symbols that require regions with high contrast relative to one another, such as light zones 302 and dark zones 304, to enable good legibility. Conventionally, good legibility has been attained by printing black ink onto a light-colored label, a process that generally yields a strong contrast. This conventional approach involves a two-step process to first produce the label—namely, make the paper tab and mark it accordingly—and second to attach the label to the component. Process deviations in design and manufacturing, and in field use including the malicious influences described above, can introduce unpredictable errors or result in tampering activities.

FIG. 3B is an example of a 2-D matrix code 306 that contains identical data to matrix code 300 of FIG. 3A. The matrices 300 and 306 have the same pattern and internal shapes, but differ with respect to their bright-dark polarity. For example, shapes 306A and 306B are the same but with dark inverted to light and vice versa. Similarly, shapes 308A and 308B are the same but with light inverted to dark and vice versa. This general polarity reversal pattern persists throughout matrices 300 and 306. The matrix polarity is not relevant to the reader in the sense that both polarities may be interpreted to read as the same data text. In short, information is conveyed in these labels because the dark and light zones in the matrices 300 and 306 create contrast when viewed by a barcode reader. This information may be included in the location of the light-dark transitions.

In one aspect of the disclosure, a 3-D machine-readable data matrix integrated into an AM component is disclosed. In an embodiment, the techniques as described herein integrate the steps described above with reference to FIGS. 3A-B into a single AM step and permanently integrate the data matrix into the shape of the original AM component. In the initial design process, a 3-D matrix pattern may be integrated into a part. This is accomplished by projecting a 2-D pattern of the matrix into 3-D space and onto the plane of an identified surface of a 3-D data model. Using this technique, the matrix pattern can be translated and rotated to be rendered consistent with the part's relevant surface facet. In an embodiment, a part may be scaled in order to accommodate a relatively flat facet area for this purpose.

FIG. 4A shows a 2-D AM matrix 400 projected onto a 3-D surface facet in accordance with an embodiment. The 2-D matrix 400 is shown segmented into black and white zones 402. However, it will be appreciated that colors may in some instances not be realizable or available during the AM process. The projection of FIG. 4A may be accomplished by selecting a desirable flat surface facet area on the 3-D component to be additively manufactured and projecting the matrix pattern onto the surface facet of the 3-D model.

In an embodiment, the particular location on the part is selected for its visibility and accessibility, whether with the part in isolation or with the part as assembled into its operating position within a larger structure, such as a vehicle. This facet selection may involve tradeoffs or design compromises. For example, the part as designed may not have a suitable surface facet. In this case, the part may in some instances be redesigned to incorporate a flat area to accommodate this feature. Further, while there may be accessible and visible flat surface facets for the isolated part, the part as assembled into a larger structure may not have any accessible such surface facets. Accordingly, in some embodiments, various design changes may be considered and implemented in order to facilitate a view of the matrix on the part. In short, changes may be required to present a suitably visible surface facet to act as a machine-readable label or nameplate on a moving part. These and other aspects may be taken into consideration during the design phase of the AM process.

In one embodiment, the matrices generated may be compatible with all reader types, including a barcode type reader, LED and laser illumination readers, ambient techniques, lens-based imaging, scanning, and the like.

FIG. 4B shows a 2-D matrix 404 projected onto a relatively flat surface facet of the 3-D model through translation, scaling, and rotation. After these steps, the projected matrix has been elevated such that regions corresponding to light zones 406A (FIG. 4A) have been elevated as shown by region 406B (FIG. 4B) and may be 3-D printed at the part surface 410. Conversely, regions corresponding to dark zones 408A (FIG. 4A) may be inset as shown by region 408B (FIG. 4B) into the part surface 410. In an embodiment, readable contrast may be created using any one or more of three techniques. First, a surface texture differential may be generated to reflect illumination differently (see FIGS. 8-11). Second, shadows may be generated in areas of varying elevation using differential block illumination. For this technique, elevations above and below the surface may cast shadows that darken certain areas such that, for example, upper surfaces may be illuminated and appear bright, while lower surfaces may be shadowed and appear dark. Vertical wall surfaces may be obscured by relatively sharp normal reading angles relative to the surface of the projected matrix. Third, coloration or infill such as paint may be used as a post-processing step.

As is evident from matrix 404 of FIG. 4B, the dark zones 408B of the matrix pattern may be projected downward vertically to form a prismatic indentation below the part surface. This effect forms a relieved 3-D representation of the matrix into the surface of the data model. The resulting matrix 404 may resemble a carving on the part surface. Inset dark zones 408B may create shadows that produce matrix contrast. Light zones 406B may remain elevated at surface level and may cast shadows into the dark zones 408B. This shadowing effect exploits the offset between the illuminator and the sensor, which offset may vary but in some embodiments may be approximately 15-45 degrees. In general, at larger illumination offsets and deeper inset distances, this shadowing effect may create readable contrast.

FIG. 5 is an example of a code pitch dimension 502 as measured on the 2-D matrix 500 of FIG. 3A. Code pitch dimension 502 is defined as the smallest discrete unit on the matrix. Code pitch dimension 502 determines the spatial resolution of the encoded information in the matrix. This dimension generally sets the limit of standoff distance and readability for barcode readers and camera sensors that can read the data in the matrix 500.

FIG. 6 is a cross-sectional view of an extruded 3-D printed matrix 600 made integral to an AM component. Code pitch dimension CP (602) is represented in this embodiment by a cross-sectional width of the smallest feature in the matrix. FIG. 6 also shows relief depth dimension RDD (604), which defines the well depth to which the matrix pattern may be inset below the AM component surface. In this exemplary embodiment, the relief depth dimension is two code-pitch lengths such that RDD=2×CP. Thus, in one embodiment, the inset below the AM part surface may be set such that the relief depth dimension is twice the code pitch. It will be appreciated that this quantity is illustrative in nature, and other dimensions may be equally suitable.

While a native matrix material may be selected and used during the component's AM process to form the readable matrix pattern, this pattern may not provide an optical contrast between the matrix and base 606. That is, because these homogenous zones exhibit no contrast between dark and light areas, the matrix may not be readable to a sensor or barcode reader. For example, in the cases of black-on-black or white-on-white marking, the low contrast may not be visible. Thus, in accordance with another aspect of the disclosure, contrast-enhancing features may be incorporated into the component data model and integrated with the AM process of the component.

FIG. 7 is a sensor view of an extruded 3-D matrix 700 with a flat top upper surface. The matrix 700 includes light zone 702 and dark zone 704 and has a similar pattern as the matrix of FIG. 3A. In this example, the matrix pattern 700 has been formed into the homogenous material of the subject component. Dark zone 306A of FIG. 3A corresponds to dark zone 704 in FIG. 7. Similarly, light zone 308A of FIG. 3A corresponds to light zone 702 in FIG. 7. The apparent contrast between dark and light zones relative to a sensor viewing the matrix at an angle substantially orthogonal to the matrix surface may be poor, having been lost as a side-effect of the AM process. Thus, in this instance, it may be difficult or impossible for a reader to reliably find the matrix or to read the data content encoded therein.

In one embodiment, a more deeply inset pattern may be used to recover some of the lost resolution. In another embodiment, in cases where the AM component is not capable of providing a deeper pattern or where such a pattern is otherwise impractical, dark and light areas may be programmed having different textures. In an exemplary embodiment, a surface texturing contrast creation mechanism is employed. Contrast may be created, for example, through differential texturing incorporated into the surface of the AM matrix. The texture may interfere with the illumination either constructively or destructively as described hereinafter, thereby creating dark and light zones.

FIG. 8 is a cross-sectional view of a 3-D printed matrix 800 having varying surface textures. In this embodiment, dark and light areas may be programmed with different textures. The surface finish and textured topography of a textured matrix may interact with illumination of a reader to produce a strong contrast and a reliable, readable matrix symbology pattern. The matrix 800 may include a part surface 802, a specular well zone 804, and a textured zone 806. The part surface 802 may be planar or relatively flat. If no matrix were printed, the part surface 802 would extend uniformly flat across the top of the figure. In this example, the matrix 800 is formed entirely by eliminating material below the part surface 802. However, in other embodiments, matrices can also be formed at the part surface 802 or elevated and protruding above the part surface 802. Recession below the surface to well zones 804 may be advantageous in some instances because the indented shape of textured zones 806 may provide protection of the matrix symbols and patterns against frictional wear and tear and other trauma.

The textured zones 806 may reflect light at different angles than the more indented well zones 804. This effect may be enhanced at certain viewing angles as a result of the obscuration and shadowing caused by matrix recession. The relieved geometry of the matrix 800 and its textures may be arranged to create visible contrast under most lighting conditions with both structured and ambient light. Effective contrast may therefore be achieved under a variety of common viewing conditions and the matrix reading process may be made more reliable as a result.

FIG. 9 shows a code-reading operation of a textured matrix 900 in accordance with the present disclosure. An illumination source 902 may generate light 903 that is directed onto the AM component and corresponding matrix 900. The matrix 900 may include a plurality of canted ridges 906 and recessed wells 908. The light 902 may be reflected differentially off canted ridges 906 and recessed wells 908. The reflected light 920 may thereupon be scattered differentially by the two textures and respective elevations. This scattering may create a contrasting pattern between the light and dark zones of the matrix 900.

The pattern of reflected light and dark areas constituting the matrix symbology may be received by sensor 904. Thereupon, an image of the matrix pattern may be formed in the sensor and processed to extract its symbolic content and geometric metadata including, for example, 3-D position, 3-D orientation, etc.

FIGS. 10A-C show an illustration of variations of the matrix code reading process from three perspectives. FIGS. 10A-C show illuminators 1002A-C and sensors 1004A-C, respectively. Referring initially to the example of FIG. 10A, matrix 1010 includes textured surfaces 1008 and specular well zones 1006. As shown, sensor 1004A is at an offset viewing angle relative to matrix 1010 and illuminator 1002A is on axis with sensor 1004A such that illuminator 1002A directs light to matrix 1010 at substantially the same angle as sensor 1004A receives it. The sensor 1004A and illuminator 1002A are in a nearly coaxial position and the field of illumination is nearly coincident with the field of view of sensor 1004A. Thus, because the light 1012 is coincident with textured surfaces 1008, reflected light 1014 from textured surfaces 1008 is bright and specular well zones 1006 are dark, which may correspond to a normal polarity.

More specifically, as illuminator 1002A emits light 1012, the textured surface 1008 of matrix 1010 may reflect light 1014 from bright zones into the sensor 1004A and the specular well zones 1006 may deflect light 1016 in a different direction. A readable contrast is thereby achieved.

Referring to FIG. 10B, sensor 1004B is at a normal (orthogonal) viewing angle relative to matrix 1020 and illumination of light 1014 from illuminator 1002B is on axis with sensor 1004B. Thus, because the direction of light 1014 is normal to specular well zones 1006 rather than the textured surfaces 1008, the textured surfaces 1008 in this embodiment are dark, the specular well zones 1006 are bright, and the polarity is inverted. Here, light 1022 from the specular well zones 1006 may be reflected directly back to sensor 1004B. The dashed lines 1016 represent the angle of any illumination differentially reflected from dark textured surfaces 1008, which is advantageously different from the angle at which light 1022 is reflected from bright specular well zones 1006. A good readable contrast may be achieved in this example as well.

Referring to FIG. 10C, sensor 1004C is at a normal viewing angle relative to specular matrix 1030 and specular well zones 1006. Illuminator 1002C is off-axis relative to sensor 1004C by an angle θ. In this example, light 1032 from illuminator 1002C arrives at angle θ substantially coincident with textured surfaces 1008. The textured surfaces 1008 consequently may reflect light 1034 directly toward the viewing angle of sensor 1004. By contrast, light 1030 reflected from the specular wells 1006 may be differentially reflected away from a viewing angle of sensor 1004C. Thus, in this embodiment, textured surfaces 1008 are bright, specular wells 1006 are dark and the polarity is normal. A good readable contrast may be achieved in this situation.

In another aspect of the disclosure, a technique for reducing contamination exposure in 3-D integrated matrices is disclosed. In addition to creating visible contrast, matrix recession such as in the embodiment of FIGS. 10A-C may improve resistance to contamination. Contamination of surfaces may be common, for example, in outdoor environments where the part may be exposed to dirt, grease, road dust and the like. Accumulated contamination may potentially obscure or interfere with the operation of sensors 1004A-C or other devices that may be used read the matrix symbology.

FIG. 11 shows a cross section of a matrix 1100 that has been exposed to contamination. The matrix includes component surface 1100, well zones 1104A-C, and textured zones 1106A-B. As is evident from the illustration, well zones 1104A-C and textured zones 1106A-B have indentations where contaminants may accumulate. Two of the well zones 1104B-C have been exposed to a contaminant 1108. Similarly, one of the textured zones 1106b has been exposed to a contaminant 1110. The contaminating substance may include, for example, substances like dirt, grease, mud, or other fine granular foreign substances. The contaminating substance may further include some mixture of organic and inorganic materials.

As is further evident from the illustration, one of well zones 1104A and one of textured zones 1106A are free from contamination. Well zone 1104A is deeper than the indentations in textured zones 1106A-B. Further, well zone 1104C is wider than the indentations in textured zones 1106A-B. For these reasons, well zones 1104A and 1104C may be able to sustain a greater accumulated bulk of contaminated material before becoming corrupted. Similarly, well zones 1104A and 1104C may also accommodate larger particles with larger overall grain size. Because the identified well zones are deeper and/or wider, the bulk of contamination may therefore accumulate in the well zones 1104A and 1104C and not in the textured surfaces 1106A-B.

Particulate matter generally scatters light broadly, as opposed to the relatively flat and smooth specular surfaces of the AM component. The small amount of contaminated material 1110 captured in textured surface 1106B may therefore be relatively insignificant. This feature provides an inherent difference in optical characteristics of the relative surfaces, which in turn may translate to a strong contrast between the light and dark zones. To this end, accumulated contamination may actually reinforce and improve the readability of the matrix symbology produced according to these embodiments. This optical feature further reinforces immunity to contamination and therefore overall reliability of the integrated matrix.

The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these exemplary embodiments presented throughout this disclosure will be readily apparent to those skilled in the art, and the concepts disclosed herein may be applied to other techniques for integrating 3-D matrices into parts. Thus, the claims are not intended to be limited to the exemplary embodiments presented throughout the disclosure, but are to be accorded the full scope consistent with the language claims. All structural and functional equivalents to the elements of the exemplary embodiments described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 112(f), or analogous law in applicable jurisdictions, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.”

Claims

1. A method of integrating a machine-readable matrix with a component of a mechanical structure using three-dimensional (3-D) printing, comprising:

generating at least one data model representing the component; and
projecting a matrix pattern identifying one or more features of the component onto a selected surface portion of the component to produce a modified data model for use as an input to a 3-D printer, wherein projecting the matrix pattern comprises integrating a 3-D matrix pattern into the selected surface to produce the modified data model, and wherein integrating the 3-D matrix pattern into the selected surface comprises selectively eliminating material below the selected surface to form the 3-D matrix pattern.

2. The method of claim 1, further comprising 3-D printing the component including the projected matrix pattern using the modified data model.

3. The method of claim 1, wherein the projecting the matrix pattern comprises at least one of translating, scaling, and rotating the matrix pattern relative to the selected surface portion.

4. The method of claim 1, wherein the projecting the matrix pattern comprises converting a two-dimensional (2-D) matrix pattern into a 3-D matrix pattern on the selected surface portion.

5. The method of claim 1, further comprising adding a plurality of surface textures to the matrix pattern.

6. The method of claim 1, further comprising varying an elevation of selected portions of the matrix pattern to create a machine-readable contrast.

7. The method of claim 6, wherein at least one elevated portion appears brighter to a machine reader and at least one portion lower than the at least one elevated portion appears darker to the machine reader to thereby create the machine-readable contrast.

8. The method of claim 1, further comprising adding coloration to the matrix pattern.

9. The method of claim 1, further comprising adding an infill to the matrix pattern.

10. The method of claim 1, further comprising 3-D printing the component using the modified data model.

11. A method of integrating a machine-readable matrix with a component of a mechanical structure using three-dimensional (3-D) printing, comprising:

generating at least one data model representing the component; and
projecting a matrix pattern identifying one or more features of the component onto a selected surface portion of the component to produce a modified data model for use as an input to a 3-D printer, wherein projecting the matrix pattern comprises integrating a 3-D matrix pattern into the selected surface to produce the modified data model, and integrating the 3-D matrix pattern into the selected surface comprises selectively adding material above the selected surface portion to form the 3-D matrix pattern.

12. The method of claim 11, further comprising 3-D printing the component including the projected matrix pattern using the modified data model.

13. The method of claim 11, wherein the projecting the matrix pattern comprises at least one of translating, scaling, and rotating the matrix pattern relative to the selected surface portion.

14. The method of claim 11, wherein the projecting the matrix pattern comprises converting a two-dimensional (2-D) matrix pattern into a 3-D matrix pattern on the selected surface portion.

15. The method of claim 11, further comprising adding a plurality of surface textures to the matrix pattern.

16. The method of claim 11, further comprising varying an elevation of selected portions of the matrix pattern to create a machine-readable contrast.

17. The method of claim 16, wherein at least one elevated portion appears brighter to a machine reader and at least one portion lower than the at least one elevated portion appears darker to the machine reader to thereby create the machine-readable contrast.

18. The method of claim 11, further comprising adding coloration to the matrix pattern.

19. The method of claim 11, further comprising adding an infill to the matrix pattern.

20. The method of claim 11, further comprising 3-D printing the component using the modified data model.

Referenced Cited
U.S. Patent Documents
5203226 April 20, 1993 Hongou et al.
5742385 April 21, 1998 Champa
5990444 November 23, 1999 Costin
6010155 January 4, 2000 Rinehart
6096249 August 1, 2000 Yamaguchi
6140602 October 31, 2000 Costin
6250533 June 26, 2001 Otterbein et al.
6252196 June 26, 2001 Costin et al.
6318642 November 20, 2001 Goenka et al.
6365057 April 2, 2002 Whitehurst et al.
6391251 May 21, 2002 Keicher et al.
6409930 June 25, 2002 Whitehurst et al.
6468439 October 22, 2002 Whitehurst et al.
6554345 April 29, 2003 Jonsson
6585151 July 1, 2003 Ghosh
6644721 November 11, 2003 Miskech et al.
6811744 November 2, 2004 Keicher et al.
6866497 March 15, 2005 Saiki
6919035 July 19, 2005 Clough
6926970 August 9, 2005 James et al.
7152292 December 26, 2006 Hohmann et al.
7344186 March 18, 2008 Hausler et al.
7500373 March 10, 2009 Quell
7586062 September 8, 2009 Heberer
7637134 December 29, 2009 Burzlaff et al.
7710347 May 4, 2010 Gentilman et al.
7716802 May 18, 2010 Stern et al.
7745293 June 29, 2010 Yamazaki et al.
7766123 August 3, 2010 Sakurai et al.
7852388 December 14, 2010 Shimizu et al.
7908922 March 22, 2011 Zarabadi et al.
7951324 May 31, 2011 Naruse et al.
8094036 January 10, 2012 Heberer
8163077 April 24, 2012 Eron et al.
8286236 October 9, 2012 Jung et al.
8289352 October 16, 2012 Vartanian et al.
8297096 October 30, 2012 Mizumura et al.
8354170 January 15, 2013 Henry et al.
8383028 February 26, 2013 Lyons
8408036 April 2, 2013 Reith et al.
8429754 April 23, 2013 Jung et al.
8437513 May 7, 2013 Derakhshani et al.
8444903 May 21, 2013 Lyons et al.
8452073 May 28, 2013 Taminger et al.
8599301 December 3, 2013 Dowski, Jr. et al.
8606540 December 10, 2013 Haisty et al.
8610761 December 17, 2013 Haisty et al.
8631996 January 21, 2014 Quell et al.
8675925 March 18, 2014 Derakhshani et al.
8678060 March 25, 2014 Dietz et al.
8686314 April 1, 2014 Schneegans et al.
8686997 April 1, 2014 Radet et al.
8694284 April 8, 2014 Berard
8720876 May 13, 2014 Reith et al.
8752166 June 10, 2014 Jung et al.
8755923 June 17, 2014 Farahani et al.
8787628 July 22, 2014 Derakhshani et al.
8818771 August 26, 2014 Gielis et al.
8873238 October 28, 2014 Wilkins
8978535 March 17, 2015 Ortiz et al.
9006605 April 14, 2015 Schneegans et al.
9071436 June 30, 2015 Jung et al.
9101979 August 11, 2015 Hofmann et al.
9104921 August 11, 2015 Derakhshani et al.
9126365 September 8, 2015 Mark et al.
9128476 September 8, 2015 Jung et al.
9138924 September 22, 2015 Yen
9149988 October 6, 2015 Mark et al.
9156205 October 13, 2015 Mark et al.
9186848 November 17, 2015 Mark et al.
9244986 January 26, 2016 Karmarkar
9248611 February 2, 2016 Divine et al.
9254535 February 9, 2016 Buller et al.
9266566 February 23, 2016 Kim
9269022 February 23, 2016 Rhoads et al.
9327452 May 3, 2016 Mark et al.
9329020 May 3, 2016 Napoletano
9332251 May 3, 2016 Haisty et al.
9346127 May 24, 2016 Buller et al.
9389315 July 12, 2016 Bruder et al.
9399256 July 26, 2016 Buller et al.
9403235 August 2, 2016 Buller et al.
9418193 August 16, 2016 Dowski, Jr. et al.
9457514 October 4, 2016 Schwärzler
9469057 October 18, 2016 Johnson et al.
9478063 October 25, 2016 Rhoads et al.
9481402 November 1, 2016 Muto et al.
9486878 November 8, 2016 Buller et al.
9486960 November 8, 2016 Paschkewitz et al.
9502993 November 22, 2016 Deng
9525262 December 20, 2016 Stuart et al.
9533526 January 3, 2017 Nevins
9555315 January 31, 2017 Aders
9555580 January 31, 2017 Dykstra et al.
9557856 January 31, 2017 Send et al.
9566742 February 14, 2017 Keating et al.
9566758 February 14, 2017 Cheung et al.
9573193 February 21, 2017 Buller et al.
9573225 February 21, 2017 Buller et al.
9586290 March 7, 2017 Buller et al.
9595795 March 14, 2017 Lane et al.
9597843 March 21, 2017 Stauffer et al.
9600929 March 21, 2017 Young et al.
9609755 March 28, 2017 Coull et al.
9610737 April 4, 2017 Johnson et al.
9611667 April 4, 2017 GangaRao et al.
9616623 April 11, 2017 Johnson et al.
9626487 April 18, 2017 Jung et al.
9626489 April 18, 2017 Nilsson
9643361 May 9, 2017 Liu
9662840 May 30, 2017 Buller et al.
9665182 May 30, 2017 Send et al.
9672389 June 6, 2017 Mosterman et al.
9672550 June 6, 2017 Apsley et al.
9676145 June 13, 2017 Buller et al.
9684919 June 20, 2017 Apsley et al.
9688032 June 27, 2017 Kia et al.
9690286 June 27, 2017 Hovsepian et al.
9700966 July 11, 2017 Kraft et al.
9703896 July 11, 2017 Zhang et al.
9713903 July 25, 2017 Paschkewitz et al.
9718302 August 1, 2017 Young et al.
9718434 August 1, 2017 Hector, Jr. et al.
9724877 August 8, 2017 Flitsch et al.
9724881 August 8, 2017 Johnson et al.
9725178 August 8, 2017 Wang
9731730 August 15, 2017 Stiles
9731773 August 15, 2017 Gami et al.
9741954 August 22, 2017 Bruder et al.
9747352 August 29, 2017 Karmarkar
9764415 September 19, 2017 Seufzer et al.
9764520 September 19, 2017 Johnson et al.
9765226 September 19, 2017 Dain
9770760 September 26, 2017 Liu
9773393 September 26, 2017 Velez
9776234 October 3, 2017 Schaafhausen et al.
9782936 October 10, 2017 Glunz et al.
9783324 October 10, 2017 Embler et al.
9783977 October 10, 2017 Alqasimi et al.
9789548 October 17, 2017 Golshany et al.
9789922 October 17, 2017 Dosenbach et al.
9796137 October 24, 2017 Zhang et al.
9802108 October 31, 2017 Aders
9809977 November 7, 2017 Carney et al.
9817922 November 14, 2017 Glunz et al.
9818071 November 14, 2017 Jung et al.
9821339 November 21, 2017 Paschkewitz et al.
9821411 November 21, 2017 Buller et al.
9823143 November 21, 2017 Twelves, Jr. et al.
9829564 November 28, 2017 Bruder et al.
9846933 December 19, 2017 Yuksel
9854828 January 2, 2018 Langeland
9858604 January 2, 2018 Apsley et al.
9862833 January 9, 2018 Hasegawa et al.
9862834 January 9, 2018 Hasegawa et al.
9863885 January 9, 2018 Zaretski et al.
9870629 January 16, 2018 Cardno et al.
9879981 January 30, 2018 Dehghan Niri et al.
9884663 February 6, 2018 Czinger et al.
9898776 February 20, 2018 Apsley et al.
9914150 March 13, 2018 Pettersson et al.
9919360 March 20, 2018 Buller et al.
9931697 April 3, 2018 Levin et al.
9933031 April 3, 2018 Bracamonte et al.
9933092 April 3, 2018 Sindelar
9957031 May 1, 2018 Golshany et al.
9958535 May 1, 2018 Send et al.
9962767 May 8, 2018 Buller et al.
9963978 May 8, 2018 Johnson et al.
9971920 May 15, 2018 Derakhshani et al.
9976063 May 22, 2018 Childers et al.
9987792 June 5, 2018 Flitsch et al.
9988136 June 5, 2018 Tiryaki et al.
9989623 June 5, 2018 Send et al.
9990565 June 5, 2018 Rhoads et al.
9994339 June 12, 2018 Colson et al.
9996890 June 12, 2018 Cinnamon et al.
9996945 June 12, 2018 Holzer et al.
10002215 June 19, 2018 Dowski et al.
10006156 June 26, 2018 Kirkpatrick
10011089 July 3, 2018 Lyons et al.
10011685 July 3, 2018 Childers et al.
10012532 July 3, 2018 Send et al.
10013777 July 3, 2018 Mariampillai et al.
10015908 July 3, 2018 Williams et al.
10016852 July 10, 2018 Broda
10016942 July 10, 2018 Mark et al.
10017384 July 10, 2018 Greer et al.
10018576 July 10, 2018 Herbsommer et al.
10022792 July 17, 2018 Srivas et al.
10022912 July 17, 2018 Kia et al.
10027376 July 17, 2018 Sankaran et al.
10029415 July 24, 2018 Swanson et al.
10040239 August 7, 2018 Brown, Jr.
10046412 August 14, 2018 Blackmore
10048769 August 14, 2018 Selker et al.
10052712 August 21, 2018 Blackmore
10052820 August 21, 2018 Kemmer et al.
10055536 August 21, 2018 Maes et al.
10058764 August 28, 2018 Aders
10058920 August 28, 2018 Buller et al.
10061906 August 28, 2018 Nilsson
10065270 September 4, 2018 Buller et al.
10065361 September 4, 2018 Susnjara et al.
10065367 September 4, 2018 Brown, Jr.
10068316 September 4, 2018 Holzer et al.
10071422 September 11, 2018 Buller et al.
10071525 September 11, 2018 Susnjara et al.
10072179 September 11, 2018 Drijfhout
10074128 September 11, 2018 Colson et al.
10076875 September 18, 2018 Mark et al.
10076876 September 18, 2018 Mark et al.
10081140 September 25, 2018 Paesano et al.
10081431 September 25, 2018 Seack et al.
10086568 October 2, 2018 Snyder et al.
10087320 October 2, 2018 Simmons et al.
10087556 October 2, 2018 Gallucci et al.
10099427 October 16, 2018 Mark et al.
10100542 October 16, 2018 GangaRao et al.
10100890 October 16, 2018 Bracamonte et al.
10107344 October 23, 2018 Bracamonte et al.
10108766 October 23, 2018 Druckman et al.
10113600 October 30, 2018 Bracamonte et al.
10118347 November 6, 2018 Stauffer et al.
10118579 November 6, 2018 Lakic
10120078 November 6, 2018 Bruder et al.
10124546 November 13, 2018 Johnson et al.
10124570 November 13, 2018 Evans et al.
10137500 November 27, 2018 Blackmore
10138354 November 27, 2018 Groos et al.
10144126 December 4, 2018 Krohne et al.
10145110 December 4, 2018 Carney et al.
10151363 December 11, 2018 Bracamonte et al.
10152661 December 11, 2018 Kieser
10160278 December 25, 2018 Coombs et al.
10161021 December 25, 2018 Lin et al.
10166752 January 1, 2019 Evans et al.
10166753 January 1, 2019 Evans et al.
10171578 January 1, 2019 Cook et al.
10173255 January 8, 2019 TenHouten et al.
10173327 January 8, 2019 Kraft et al.
10178800 January 8, 2019 Mahalingam et al.
10179640 January 15, 2019 Wilkerson
10183330 January 22, 2019 Buller et al.
10183478 January 22, 2019 Evans et al.
10189187 January 29, 2019 Keating et al.
10189240 January 29, 2019 Evans et al.
10189241 January 29, 2019 Evans et al.
10189242 January 29, 2019 Evans et al.
10190424 January 29, 2019 Johnson et al.
10195693 February 5, 2019 Buller et al.
10196539 February 5, 2019 Boonen et al.
10197338 February 5, 2019 Melsheimer
10200677 February 5, 2019 Trevor et al.
10201932 February 12, 2019 Flitsch et al.
10201941 February 12, 2019 Evans et al.
10202673 February 12, 2019 Lin et al.
10204216 February 12, 2019 Nejati et al.
10207454 February 19, 2019 Buller et al.
10209065 February 19, 2019 Estevo, Jr. et al.
10210662 February 19, 2019 Holzer et al.
10213837 February 26, 2019 Kondoh
10214248 February 26, 2019 Hall et al.
10214252 February 26, 2019 Schellekens et al.
10214275 February 26, 2019 Goehlich
10220575 March 5, 2019 Reznar
10220881 March 5, 2019 Tyan et al.
10221530 March 5, 2019 Driskell et al.
10226900 March 12, 2019 Nevins
10232550 March 19, 2019 Evans et al.
10234342 March 19, 2019 Moorlag et al.
10237477 March 19, 2019 Trevor et al.
10252335 April 9, 2019 Buller et al.
10252336 April 9, 2019 Buller et al.
10254499 April 9, 2019 Cohen et al.
10257499 April 9, 2019 Hintz et al.
10259044 April 16, 2019 Buller et al.
10268181 April 23, 2019 Nevins
10269225 April 23, 2019 Velez
10272860 April 30, 2019 Mohapatra et al.
10272862 April 30, 2019 Whitehead
10275564 April 30, 2019 Ridgeway et al.
10279580 May 7, 2019 Evans et al.
10285219 May 7, 2019 Fetfatsidis et al.
10286452 May 14, 2019 Buller et al.
10286603 May 14, 2019 Buller et al.
10286961 May 14, 2019 Hillebrecht et al.
10289263 May 14, 2019 Troy et al.
10289875 May 14, 2019 Singh et al.
10291193 May 14, 2019 Dandu et al.
10294552 May 21, 2019 Liu et al.
10294982 May 21, 2019 Gabrys et al.
10295989 May 21, 2019 Nevins
10303159 May 28, 2019 Czinger et al.
10307824 June 4, 2019 Kondoh
10310197 June 4, 2019 Droz et al.
10313651 June 4, 2019 Trevor et al.
10315252 June 11, 2019 Mendelsberg et al.
10336050 July 2, 2019 Susnjara
10337542 July 2, 2019 Hesslewood et al.
10337952 July 2, 2019 Bosetti et al.
10339266 July 2, 2019 Urick et al.
10343330 July 9, 2019 Evans et al.
10343331 July 9, 2019 McCall et al.
10343355 July 9, 2019 Evans et al.
10343724 July 9, 2019 Polewarczyk et al.
10343725 July 9, 2019 Martin et al.
10350823 July 16, 2019 Rolland et al.
10356341 July 16, 2019 Holzer et al.
10356395 July 16, 2019 Holzer et al.
10357829 July 23, 2019 Spink et al.
10357957 July 23, 2019 Buller et al.
10359756 July 23, 2019 Newell et al.
10369629 August 6, 2019 Mendelsberg et al.
10382739 August 13, 2019 Rusu et al.
10384393 August 20, 2019 Xu et al.
10384416 August 20, 2019 Cheung et al.
10389410 August 20, 2019 Brooks et al.
10391710 August 27, 2019 Mondesir
10392097 August 27, 2019 Pham et al.
10392131 August 27, 2019 Deck et al.
10393315 August 27, 2019 Tyan
10400080 September 3, 2019 Ramakrishnan et al.
10401832 September 3, 2019 Snyder et al.
10403009 September 3, 2019 Mariampillai et al.
10406750 September 10, 2019 Barton et al.
10412283 September 10, 2019 Send et al.
10416095 September 17, 2019 Herbsommer et al.
10421496 September 24, 2019 Swayne et al.
10421863 September 24, 2019 Hasegawa et al.
10422478 September 24, 2019 Leachman et al.
10425793 September 24, 2019 Sankaran et al.
10427364 October 1, 2019 Alves
10429006 October 1, 2019 Tyan et al.
10434573 October 8, 2019 Buller et al.
10435185 October 8, 2019 Divine et al.
10435773 October 8, 2019 Liu et al.
10436038 October 8, 2019 Buhler et al.
10438407 October 8, 2019 Pavanaskar et al.
10440351 October 8, 2019 Holzer et al.
10442002 October 15, 2019 Benthien et al.
10442003 October 15, 2019 Symeonidis et al.
10449696 October 22, 2019 Elgar et al.
10449737 October 22, 2019 Johnson et al.
10461810 October 29, 2019 Cook et al.
20060108783 May 25, 2006 Ni et al.
20070152032 July 5, 2007 Tuschel
20100055423 March 4, 2010 Chretien
20140263674 September 18, 2014 Cerveny
20140277669 September 18, 2014 Nardi et al.
20160260001 September 8, 2016 Flores
20170113344 April 27, 2017 Schönberg
20170341309 November 30, 2017 Piepenbrock et al.
Foreign Patent Documents
2015082678 April 2015 JP
1996036455 November 1996 WO
1996036525 November 1996 WO
1996038260 December 1996 WO
2003024641 March 2003 WO
2004108343 December 2004 WO
2005093773 October 2005 WO
2007003375 January 2007 WO
2007110235 October 2007 WO
2007110236 October 2007 WO
2008019847 February 2008 WO
2007128586 June 2008 WO
2008068314 June 2008 WO
2008086994 July 2008 WO
2008087024 July 2008 WO
2008107130 September 2008 WO
2008138503 November 2008 WO
2008145396 December 2008 WO
2009083609 July 2009 WO
2009098285 August 2009 WO
2009112520 September 2009 WO
2009135938 November 2009 WO
2009140977 November 2009 WO
2010125057 November 2010 WO
2010125058 November 2010 WO
2010142703 December 2010 WO
2011032533 March 2011 WO
2014016437 January 2014 WO
2014187720 November 2014 WO
2014195340 December 2014 WO
2015193331 December 2015 WO
2016116414 July 2016 WO
2017036461 March 2017 WO
2019030248 February 2019 WO
2019042504 March 2019 WO
2019048010 March 2019 WO
2019048498 March 2019 WO
2019048680 March 2019 WO
2019048682 March 2019 WO
Other references
  • US 9,202,136 B2, 12/2015, Schmidt et al. (withdrawn)
  • US 9,809,265 B2, 11/2017, Kinjo (withdrawn)
  • US 10,449,880 B2, 10/2019, Mizobata et al. (withdrawn)
Patent History
Patent number: 11001047
Type: Grant
Filed: Jun 21, 2019
Date of Patent: May 11, 2021
Patent Publication Number: 20190315116
Assignee: Divergent Technologies, Inc. (Los Angeles, CA)
Inventors: Donald J. Christian (Los Angeles, CA), John Russell Bucknell (Los Angeles, CA)
Primary Examiner: Leo B Tentoni
Application Number: 16/449,216
Classifications
Current U.S. Class: With Printing Or Coating Of Workpiece (out Of Mold) (264/129)
International Classification: B05D 1/00 (20060101); B29C 41/22 (20060101); B33Y 10/00 (20150101); G06K 1/12 (20060101); G06K 19/06 (20060101); B33Y 50/02 (20150101); G06T 7/73 (20170101); B29C 64/393 (20170101); B33Y 99/00 (20150101); G06F 17/00 (20190101); B33Y 50/00 (20150101); B29C 64/386 (20170101); B33Y 80/00 (20150101); G06F 30/17 (20200101); G06F 30/3323 (20200101); B29C 64/118 (20170101); B29C 64/153 (20170101); B29C 67/00 (20170101); B29C 69/00 (20060101);